Yulia B. Monakhova

Application of automated eightfold suppression of water and ethanol signals in 1H NMR to provide sensitivity for analyzing alcoholic beverages

The 400 MHz 1H NMR analysis of alcoholic beverages using standard pulse programs lacks the necessary sensitivity to detect minor constituents such as methanol, acetaldehyde or ethyl acetate. This study investigates the application of a shaped pulse sequence during the relaxation delay to suppress the eight 1H NMR frequencies of water and ethanol (the OH singlet of both water and ethanol, as well as the CH2 quartet and CH3 triplet of ethanol). The sequence of reference measurement for frequency determination followed by the suppression experiment is controlled by a macro in the acquisition software so that a measurement under full automation is possible (12 min per sample total time). Additionally, sample preparation was optimized to avoid precipitation, which is facilitated by 1 : 1 dilution with ethanol and pH 7.4 buffer.

Synergistic effect of the simultaneous chemometric analysis of 1H NMR spectroscopic and stable isotope (SNIF-NMR, 18O, 13C) data: Application to wine analysis

It is known that (1)H NMR spectroscopy represents a good tool for predicting the grape variety, the geographical origin, and the year of vintage of wine. In the present study we have shown that classification models can be improved when (1)H NMR profiles are fused with stable isotope (SNIF-NMR, (18)O, (13)C) data. Variable selection based on clustering of latent variables was performed on (1)H NMR data. Afterwards, the combined data of 718 wine samples from Germany were analyzed using linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), factorial discriminant analysis (FDA) and independent components analysis (ICA). Moreover, several specialized multiblock methods (common components and specific weights analysis (ComDim), consensus PCA and consensus PLS-DA) were applied to the data. The best improvement in comparison with (1)H NMR data was obtained for prediction of the geographical origin (up to 100% for the fused data, whereas stable isotope data resulted only in 60-70% correct prediction and (1)H NMR data alone in 82-89% respectively). Certain enhancement was obtained also for the year of vintage (from 88 to 97% for (1)H NMR to 99% for the fused data), whereas in case of grape varieties improved models were not obtained. The combination of (1)H NMR data with stable isotope data improves efficiency of classification models for geographical origin and vintage of wine and can be potentially used for other food products as well.

Qualitative and QuantitativeControl of CarbonatedCola Beverages Using 1H NMR Spectroscopy

1H Nuclear magnetic resonance (NMR) spectroscopy (400 MHz) was used in the context of food surveillance to develop a reliable analytical tool to differentiate brands of cola beverages and to quantify selected constituents of the soft drinks. The preparation of the samples required only degassing and addition of 0.1% of TSP in D2O for locking and referencing followed by adjustment of pH to 4.5. The NMR spectra obtained can be considered as “fingerprints” and were analyzed by principal component analysis (PCA). Clusters from colas of the same brand were observed, and significant differences between premium and discount brands were found. The quantification of caffeine, acesulfame-K, aspartame, cyclamate, benzoate, hydroxymethylfurfural (HMF), sulfite ammonia caramel (E 150D), and vanillin was simultaneously possible using external calibration curves and applying TSP as internal standard. Limits of detection for caffeine, aspartame, acesulfame-K, and benzoate were 1.7, 3.5, 0.8, and 1.0 mg/L, respectively. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of soft drinks and quantification of selected constituents.

Unrecorded alcohol consumption in Russia: toxic denaturants and disinfectants pose additional risks

Unrecorded alcohol consumption in Russia: toxic denaturants and disinfectants pose additional risks In 2005, 30% of all alcohol consumption in Russia was unrecorded. This paper describes the chemical composition of unrecorded and low cost alcohol, including a toxicological evaluation. Alcohol products (n=22) from both recorded and unrecorded sources were obtained from three Russian cities (Saratov, Lipetsk and Irkutsk) and were chemically analyzed. Unrecorded alcohols included homemade samogons, medicinal alcohols and surrogate alcohols. Analysis included alcoholic strength, levels of volatile compounds (methanol, acetaldehyde, higher alcohols), ethyl carbamate, diethyl phthalate (DEP) and polyhexamethyleneguanidine hydrochloride (PHMG). Single samples showed contamination with DEP (275-1269 mg/l) and PHMG (515 mg/l) above levels of toxicological concern. Our detailed chemical analysis of Russian alcohols showed that the composition of vodka, samogon and medicinal alcohols generally did not raise major public health concerns other than for ethanol. It was shown, however, that concentration levels of DEP and PHMG in some surrogate alcohols make these samples unfit for human consumption as even moderate drinking would exceed acceptable daily intakes.

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright

Nuclear magnetic resonance spectroscopy and chemometrics to identify pine nuts that cause taste disturbance.

Nontargeted 400 MHz (13)C and (1)H nuclear magnetic resonance (NMR) spectroscopy was used in the context of food surveillance to reveal Pinus species whose nuts cause taste disturbance following their consumption, the so-called pine nut syndrome (PNS). Using principal component analysis, three groups of pine nuts were distinguished. PNS-causing products were found in only one of the groups, which however also included some normal products. Sensory analysis was still required to confirm PNS, but NMR allowed the sorting of 53% of 57 samples, which belong to the two groups not containing PNS species. Furthermore, soft independent modeling of class analogy was able to classify the samples between the three groups. NMR spectroscopy was judged as suitable for the screening of pine nuts for PNS. This process may be advantageous as a means of importation control that will allow the identification of samples suitable for direct clearance and those that require further sensory analysis.

Rapid approach to identify the presence of Arabica and Robusta species in coffee using 1H NMR spectroscopy

NMR spectroscopy was used to verify the presence of Arabica and Robusta species in coffee. Lipophilic extracts of authentic roasted and green coffees showed the presence of established markers for Robusta (16-O-methylcafestol (16-OMC)) and for Arabica (kahweol). The integration of the 16-OMC signal (δ 3.165 ppm) was used to estimate the amount of Robusta in coffee blends with an approximate limit of detection of 1-3%. The method was successfully applied for the analysis of 77 commercial coffee samples (coffee pods, coffee capsules, and coffee beans). Furthermore, principal component analysis (PCA) was applied to the spectra of lipophilic and aqueous extracts of 20 monovarietal authentic samples. Clusters of the two species were observed. NMR spectroscopy can be used as a rapid prescreening tool to discriminate Arabica and Robusta coffee species before the confirmation applying the official method.

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non‐negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple‐to‐use‐interactive self‐modeling mixture analysis (SIMPLISMA) and multivariate curve resolution‐alternating least squares (MCR‐ALS)] are applied for simultaneous 1H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight‐component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography–mass spectrometry as well as the MCR‐ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures. Copyright

Combined chemometric analysis of 1H NMR, 13C NMR and stable isotope data to differentiate organic and conventional milk

The increased sales of organically produced food create a strong need for analytical methods, which could authenticate organic and conventional products. Combined chemometric analysis of (1)H NMR-, (13)C NMR-spectroscopy data, stable-isotope data (IRMS) and α-linolenic acid content (gas chromatography) was used to differentiate organic and conventional milk. In total 85 raw, pasteurized and ultra-heat treated (UHT) milk samples (52 organic and 33 conventional) were collected between August 2013 and May 2014. The carbon isotope ratios of milk protein and milk fat as well as the α-linolenic acid content of these samples were determined. Additionally, the milk fat was analyzed by (1)H and (13)C NMR spectroscopy. The chemometric analysis of combined data (IRMS, GC, NMR) resulted in more precise authentication of German raw and retail milk with a considerably increased classification rate of 95% compared to 81% for NMR and 90% for IRMS using linear discriminate analysis.

Qualitative and Quantitative Control of Honeys Using NMR Spectroscopy and Chemometrics

400 MHz nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis techniques were used in the context of food surveillance to measure 328 honey samples with 1H and 13C NMR. Using principal component analysis (PCA), clusters of honeys from the same botanical origin were observed. The chemical shifts of the principal monosaccharides (glucose and fructose) were found to be mostly responsible for this differentiation. Furthermore, soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) could be used to automatically classify spectra according to their botanical origin with 95–100% accuracy. Direct quantification of 13 compounds (carbohydrates, aldehydes, aliphatic and aromatic acids) was additionally possible using external calibration curves and applying TSP as internal standard. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of botanical origin and quantification of selected constituents of honeys.